A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers

Abstract

Peer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-topeer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO 2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO 2 emissions and up to 80.08% in traffic congestion.

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

“…Nowadays, the massive use of apps and interconnected smartphones facilitates the immediate contact between drivers and users for sharing trips. Furthermore, ride-sharing activities provide multiple benefits for drivers, users, and the entire community [88], such as the reduction in costs, pollution, and traffic congestion.…”

Predicting Requests in Large-Scale Online P2P Ridesharing

“…We consider the P2P-RS problem as defined in [3], i.e., as an online stochastic scheduling problem [8] over a discrete time horizon H = [1, h]. Our problem takes place in an area divided in n zones, i.e., Z = {1, .…”

“…where ρ CO 2 , ρ noise , and ρ traffic measure the importance of the corresponding environmental benefit and ρ QoS that of the QoS. We refer the interested reader to [3] for further technical details.…”

“…In this paper we tackle the fundamental question of assessing the benefit of predictions in the context of P2P-RS optimisation. Specifically, we present our preliminary study that aims at including predictions in our P2P-RS solution technique [3], with the objective of improving the total reward of the computed solution. Results on a public real-world dataset [7] show that, by employing a perfect predictor, the total reward can be improved by 5.27% (6.31% during weekend days) with a forecast horizon of 1 minute.…”

Analysis of Carbon Dioxide Emissions From Road Transport Using Taxi Trips

“…Hence, there is an urgent need to explore causes and possible solutions to slow the growth of human-made emissions of carbon dioxide. Reducing emissions from this sector can be accomplished by increased public transport use, incentives for renewing the vehicle fleet, and shared mobility [1]. A considerable amount of CO2 emissions can be reduced by better managing fuel consumption.…”